Some have said, that in the final analysis, all we truly have is our stories. But without compassion, these stories are suspiciously one-sided. Compassion gives stories gravitas and inspire us to look deeper into their relevance. Compassion serves as a convergence point for gravitas, around specific concepts. By expressing concepts in a variety of manners, the gravitas of compassion blossoms into passions, and passions, sufficiently validated for consistency, illuminate the cognitive basis of truth.
Compassion and its underlying sentiments are thus foundations of memorable expressive language. Even short passages convey story lines which express compassion. Extensive research into story line analysis, using accurate word-by-word sentiment analysis, shows that story lines gather tension and resolve tension in passages as short as a few sentences, to foreshadow or resonate with more significant themes. Literary editors have long known that foreshadowing a theme can make a story more memorable, especially when an item, not logically part of the theme, foreshadows a theme, thus increasing readers' suspense, to find out how the item fits into the theme.
Cognitive psychology research confirms this, showing that memory retention is strongly influenced by emotional valence, so that items with positive or negative valence are better remembered than neutral items. As readers continue reading a story, they retain memory of the items with greatest valence, and from these items, elaborate possible ways that foreshadowed sentiments fit into known themes. Cognitive psychology research also confirms that items are recalled by people more clearly, whenever items resonate with larger mental themes, thus enabling people's minds to elaborate connections to these larger themes.
Stories of compassion are among the deepest and more meaningful elaborations of themes, such as memories of saving lives, saving our self-respect, saving the meaning of our existence. However, traditional search engine indices have ignored compassion and even sentiment, since scientific tradition has biased research toward analysis of logic, grammar, and Aristotelian hierarchy. Sentiment is clearly independent of, and scarcely follows these rigid structures, for as cognitive research shows, sentiment continues to operate within many patients whose capabilities of logic, grammar and speech have vanished, showing that sentiment operates on a cognitive level more fundamental than levels of logic, grammar, and Aristotelian hierarchy.
For example, our logical scientific tradition provide no practical efficient way to recognize the negative sentiment in sentences such as this: “This hotel has a policy which, if another guest overstays their reservation, allows them to change your reservation, and check you into a different room the size of a large bathtub.” Yes, there are costly impractical methods, such as modeling the grammar and real-world sizes of all dictionary nouns, together with creating models of all possible expectations related to hotel stays. However, it would be more practical and cost-effective to sense the complaining tone of this sentence, which arises from the word order and sound of the words, and conveys sentiment directly through musical and repetitive cadences of language.
Consider that, from birth, humans are imprinted with sounds of baby-talk, a kind of rhythmic encouraging cooing soothing, or stoccato warning, or rising and falling tones of encouragement. Mommies and daddies use this pre-verbal speech to communicate with babies before they can talk, to convey dramatic messages or soothings in a primal compassionate way that babies can understand. Consistency with these initial rhythmic and tonal patterns of empathy and compassion persists as babies acquire language, becoming a permanent underlying component of language.
Text analytics for detecting such underlying rhythmic and tonal patterns can take a variety of forms more computationally efficient and flexible for recognizing emotion and sentiment than logic, grammar and hierarchy. Regardless, however, of the sentiment detection methods used to recognize phrases of dramatic tension and resolution that elaborate themes of compassion, there is great utility in tracking dramatic tension and resolution that elaborates themes of compassion.
Even methods based on traditional manual annotation, or automated by computers to classify words by negative and positive valence, and modification of valence by sign for nearby words such as “no” and “but”, can reveal useful measurements of sentiments for many practical purposes.
For instance, in the fields of eDiscovery and social media monitoring, intentions surrounding actions are among the most valuable dramatic items to recognize. In eDiscovery, these are illegal or unethical intentions. In social media monitoring, these are intentions propelling purchasing decisions. Significantly, these actionable intentions depend greatly on the grammar of verbs, and the cluster of words around these verbs which convey the intention which makes actions illegal, or the intention behind a purchasing decision.
Similarly, from a traditional story analysis perspective, motivational intentions converge upon actions associated with people and dramatic tension and resolution components of compassion analysis. For instance, a product reviewer may have problems with one product that are overcome by switching to another product. The swings in sentiment in this story enhance a story's credibility, and a sense of compassion (about the product category) from the reviewer. One-sided stories lacking in compassion sentiments are less credible to readers. Accurately tracking the sentiments occurring around themes in stories would enable eDiscovery of credibly illegal intentions, or monitoring of credibly specific purchasing intentions.
Cognitive research shows that items with stronger sentiment valences, which are germane to tracking compassion sentiments, are more likely to be remembered. Similar to page-rank in the Google algorithm, which tags and retrieves web pages by how many pages link to them, human minds tag and retrieve memory according to how strong the sentiments are surrounding those memories. Enormous amounts of data have no page-by-page links, such as the millions of free downloadable eBooks on the web. For these books, summaries of their themes with strong sentiments would be useful, as would ratings based on compassion (wholesomeness) and variety (interestingness), especially if rated for specific concepts (themes) that readers are already curious about.
Accurate sentiment annotations are also used to seek relationships between the most salient concept relationships within text. Concentrating on relationships between salient concepts is essentially what editors do when boiling text down to its essence, for improved readability and to write catchy, pithy description to market books. In book reviews and literary criticism, compassion and variety of opinions expressed are useful measures of how useful those opinions are, as search engine results.
Search engine results quality is now crucial for consumers relying upon customer reviews. For instance, consumers read reviews, and dismiss them for bad writing, emotional bias, or suspiciously fabricated appearance. Research has shown that even the presence of spelling errors reduces consumer confidence to the point of driving purchase rates significantly downward, so that some commercial web sites have manually edited product reviews to correct spelling mistakes. Given the importance of writing quality in reviews, selectively presenting customer reviews having cohesive, unbiased, compassionate and in-depth commentary could even more powerfully promote sales and better educate consumers.
In-depth commentaries are characterized by a variety of different observations. Rather than promoting a narrow or cliched view, in-depth commentaries describe a variety of unusual aspects woven into the story of the review. Monitoring the cohesion of reviews excludes reviews whose comments are merely concatenations of unrelated opinions, which could generated by an automatic review generating program. Monitoring reviews for compassion would also exclude reviews based upon malicious or obnoxious sentiments.
Related criteria have been used by major search engines when filtering out web pages which are fabricated solely to mislead search engine algorithms. For instance, by artificially generating millions of web pages linked to target web pages, the traditional rankings based on link popularity can be forced upwards, though the links themselves can be meaningless. This technique is known as “black hat search engine optimization (SEO).”
Search engine portals such as Google therefore have spent considerable resources to review the quality of web page links. By comparing the relevance of web pages on both sides of each web link, it is useful to deprecate links in the search engine algorithm, when pages on both sides of a web link are irrelevant to each other, thus reducing the page rank of results from those links.
Similar writing standards also can bridge between literary readers and writers. Due to increasing population, literacy rates, and ease of access to publications through e-books, both writers and readers of literature have become far too numerous for publishing companies to serve as intermediaries. No longer can manual methods consistently categorize genre and quality of all literature; only automated methods can do that now. However, readability, credibility and genre thematic quality traditionally vetted by publishing houses cannot be categorized by traditional probability and keyword analysis of text analytics. Viewed through its writings, society has evolved a greater sense of compassion by positing problems, then solutions, over and over, while increasing both the variety and cohesion of this Socratic discourse. Automation to validate this social progress, concept by concept, by vigilantly reviewing blogs and social media, could be instrumental in bringing about needed social changes, by showing where human efforts and resources are most needed, on a consistent conceptual basis.